Integrated Multimodal Travel Behavior Analysis under Mobility as a Service

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Mobility as a Service (MaaS) is an important means of urban transportation sustainable development that has become popular in the global transportation field recently. By integrating services of multiple providers (e.g., taxi, rental vehicle, bicycle, public transit, car- or bike-sharing, etc.) and enabling searching, booking, and payment through a single digital platform, MaaS provides door-to-door seamless mobility solutions that allows travelers to arrive destinations as quickly as possible. However, the MaaS conceptual framework and the potential influence of MaaS on people's travel behavior are not well elaborated in Canada, and current models and analysis approaches require improvement to explain the travel decision-making rules under big data. Therefore, the long-term objective of this program is to develop innovative models, new data methods, and applicable tools to generate an efficient, intelligent, and sustainable multimodal transportation system in Canada. The content of this program includes two work packages. Package 1 develops the forefront knowledge to analyze the travel behavior decisions of travelers, considering the diversity of human factors (gender, age, region, etc.) to understand their travel preferences. Package 2 focuses on new mathematical models of travel behavior analysis to better predict decisions. The research results of package 2 can provide support for package 1 to better understand the multimodal travel decisions under MaaS. More specifically, the short-term objectives for this program are to: 1) Develop forefront behavioral models to understand consumer's decision-making under MaaS. 2) Develop multi-source data fusion techniques to integrate large-scale detector data with small-scale survey data. 3) Propose new feature selection methods to select the most important predictors for travel decisions under MaaS. 4) Develop behavioral theory-driven machine learning approaches that bridge machine learning and behavior models. This program can contribute to achieving the economic, social, and environmental goals of sustainable mobility in Canada. The research results can provide theoretical support for the government to make long-term and short-term policies on MaaS. The methodological pipeline developed is valuable to engineering domains and intelligent transport system applications, which provides an important fundament to other researchers for further development. In addition, this program will promote fundamental interdisciplinary advances to train HQP through the integration of artificial intelligence knowledge with domain expertise in transportation engineering.